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Quantum memories facilitate the storage and retrieval of quantum information for on-chip and long-distance quantum communications. Thus, they play a critical role in quantum information processing and have diverse applications ranging from aerospace to medical imaging fields. Bulk acoustic wave (BAW) phonons are attractive candidates for quantum memories because of their long lifetimes and high operating frequencies. In this study, we establish a modeling approach to design hybrid magnonic high-overtone bulk acoustic wave resonator (HBAR) structures for high-density, long-lasting quantum memories, and efficient quantum transduction devices. We illustrate the approach by investigating a hybrid magnonic system, consisting of a gadolinium iron garnet (GGG) thick film and a patterned yttrium iron garnet (YIG) thin film. The BAW phonons are excited in GGG thick film via coupling with magnons in the YIG thin film. We present theoretical and numerical analyses of the diffraction-limited BAW phonon lifetimes, modeshapes, and magnon–phonon coupling strengths in YIG/GGG planar and confocal HBAR (CHBAR) structures. We utilize Fourier beam propagation and Hankel transform eigenvalue problem methods and compare the two methods. We discuss strategies to improve the phonon lifetimes in the diffraction-limited regime, since increased lifetimes have direct implications on the storage times of quantum states for quantum memory applications. We find that ultra-high cooperativities and phonon lifetimes on the order of ∼ 10 5 and ∼ 10 milliseconds, respectively, could be achieved using a CHBAR structure with 10 μ m YIG lateral area. Additionally, high integration density of on-chip memory or transduction centers is naturally desired for high-density memory or transduction devices. The proposed CHBAR structure will offer more than 100-fold improvement of integration density relative to a recently demonstrated YIG/GGG device. Our results will have direct applicability for devices operating in the cryogenic or milliKelvin regimes. For example, our study will inform the design of HBAR devices that could couple with superconducting qubits for promising quantum information platforms. [ABSTRACT FROM AUTHOR]

ABSTRACT Current geographic knowledge graph research mainly focuses on static knowledge coupling through analysis of web text and maps, but its knowledge expression model fails to incorporate knowledge mechanisms such as process evolution and interactions. The geo‐scene is a synthesis of various geographic elements which prescribes spatial, temporal, semantic, attribute, and interrelationships. It has demonstrated its utility in facilitating geocomputation, spatiotemporal simulation and reasoning related to geo‐process. Therefore, developing a geo‐scene‐based knowledge graph is significant to improve the ability of spatiotemporal knowledge representation and service. In this study, we first analyze the basic characteristics of geo‐scenes and try to emphasize the evolution of geo‐scenes through redefining the ontologies ‘event’, ‘process’ and ‘state’. To describe the dynamic nested structure of geo‐scenes, we propose a geo‐scene knowledge representation framework, and illustrate the representation of a geo‐scene and its inner relationships. Moreover, to keep the integrity, interaction and dynamic of geo‐scenes, we develop a event‐process centered geo‐scene knowledge organization method. Furthermore, we have tested the utility of our proposed representation by the Beijing 731 heavy rainstorm. The case study represents heavy rainstorm to demonstrate the complicated structure of the disaster scene, and the support for precipitation process simulation, early warning information organization, and disaster situation information query. The proposed representation provides an idea for the construction of geo‐dynamic knowledge graphs, encodes both static and dynamic geographic knowledge, makes explicit the evolution and interaction knowledge in geo‐scenes, and expands the application service of geo‐scene knowledge graph in the era of GeoAI. [ABSTRACT FROM AUTHOR]

Working memory serves as a means to accumulate information and reorganize it. Researchers have long assumed that the natural organization of information is one stream at a time. This logic leads to the expectation that, when two different series of stimuli are to be remembered, performance should be superior if the series are presented one before the other in succession, rather than concurrently. Moreover, different accounts of attentional limits lead to different expectations for the change in the ability to encode two sets across age groups in childhood. Testing children from first grade (6–7 years) to adulthood, we presented sequences of colored objects and tones in succession or concurrently (with one color accompanying an unrelated tone) and found that performance was equally good no matter which presentation method was used. The results for both presentation methods closely matched the intricate pattern of development observed by Cowan et al. (2018), who used successive presentation only. We found marked developmental improvement in the ability to retain materials in each modality without an increasing cost of attention‐sharing between modalities. Humans at least from the elementary school years through young adulthood thus display an ability to accommodate and organize two concurrent streams of information. Research Highlights: Memory for stimuli from multiple modalities is relevant to school performance and learning; here we investigate how attention is shared between remembering colors and tones.Participants received four colors and/or four tones for subsequent recognition on a trial, with dual modalities presented successively (0.5 s per stimulus) or concurrently (0.5 s per pair).Successive versus concurrent presentation had little effect on recognition, and the marked increase in memory performance with age did not come from dividing attention during encoding or maintenance.Children as young as first grade thus can encode and organize for later recognition colors and concurrently‐presented, but unrelated, tones. [ABSTRACT FROM AUTHOR]

Currently, online learning systems in the education sector are widely used and have become a new trend, generating large amounts of educational data based on students' activities. In order to improve online learning experiences, sophisticated data analysis techniques are required. Adding value to E-learning platforms through the efficient processing of big learning data is possible with Big Data. With time, the E-learning management system's repository expands and becomes a rich source of learning materials. Subject matter experts may benefit from using E-learning resources to reuse previously created content when creating online content. In addition, it might be beneficial to the students by giving them access to the pertinent documents for achieving their learning objectives effectively. An improved intelligent information retrieval and reliable storage (OIIRS) scheme is proposed for E-learning using hybrid deep learning techniques. Assume that relevant E-learning documents are stored in cloud and dynamically updated according to users' status. First, we present a highly robust and lightweight crypto, i.e., optimized CLEFIA, for securely storing data in local repositories that improve the reliability of data loading. We develop an improved butterfly optimization algorithm to provide an optimal solution for CLEFIA that selects private keys. In addition, a hybrid deep learning method, i.e., backward diagonal search-based deep recurrent neural network (BD-DRNN) is introduced for optimal intelligent information retrieval based on keywords rather than semantics. Here, feature extraction and key feature matching are performed by the modified Hungarian optimization (MHO) algorithm that improves searching accuracy. Finally, we test our proposed OIIRS scheme with different benchmark datasets and use simulation results to test the performance. [ABSTRACT FROM AUTHOR]

AbstractHead-up displays (HUDs) enable drivers to receive additional information while maintaining a forward view of the road. However, due to human cognitive resource limitations, improper HUD interface design can pose safety risks, especially when multiple pieces of information are presented simultaneously. This study involved 45 participants and conducted two driving simulation experiments. Study 1 explored the impact of different HUD interface positions on driver cognitive performance in single cognitive task conditions. Study 2 validated the findings of Study 1 under multiple cognitive task conditions and further compared drivers’ cognitive performance, workload, driving operation, and eye movement between grouped and disordered information layouts on the HUD. The results indicated that the central area of the HUD interface provided the best cognitive performance, followed by the left side, with the right side performing the poorest. Grouped information layouts on the HUD proved superior to disordered layouts in terms of cognitive performance, workload, driving operation, and eye movement. An XGBoost regression model was proposed to predict the drivers’ comprehensive cognitive responses. This study could provide empirical evidence and practical recommendations for HUD interface design. [ABSTRACT FROM AUTHOR]

Transparency is a complex and multifaceted communication phenomenon. In the current environment, demands for organizational transparency now come from a wide range of entities we term visibility agents, ranging from nongovernmental organizations (NGOs) and global networks, on one hand, to activist coalitions and automated surveillance agents, on the other. We develop a framework to conceive of such visibility agents and the range of transparency demands that they make in the context of environmental issues, positing that visibility agents significantly shape and diversify transparency practices. We identify four major relationships between visibility agents and organizations— inquisitorial, adversarial, associative, and advocative —which are associated with specific kinds of transparency demands, requests, and imperatives: accountability, monitoring, disclosure, and secrecy. We illustrate each set of relationships with examples of environmental reporting practices, one of the most prominent areas of transparency management. Implications for both theory and research on transparency are discussed. [ABSTRACT FROM AUTHOR]

Intelligent design has been the direction pursued by international electric vehicle (EV) research and development (R&D) teams in recent years. This paper analyzes the problems of unsustainable development in the current product design of EVs in China, such as high R&D investment, high innovation risks, and low R&D input–output ratios. It explores the issues related to intelligent design, R&D investment, car prices, and safety in the field of EVs in China, and it proposes the concept of optimizing intelligence to optimize the design investment of EVs in China. On the basis of the development situation and the existing problems of social organization standards that gather innovative technologies for EVs, this paper used data from the national social organization standard information platform as the research object and analyzed important data, such as the quantity of the information of relevant social organizations and professional fields of social organization standards, through mathematical methods. The article proposes an optimization design scheme for EV products in China, combining intelligence and practicality from the perspective of the optimizing intelligent design, and it models the construction of EV optimization design. The quantitative relationship between the two schemes before and after optimization design is compared in terms of cost savings in intelligent design, the improvement of social benefits, and the enhancement of EV cost performance. The comparative study found that intelligent optimization design reduced the R&D cost of EVs by 45.24%, and the social benefits of R&D investment increased by 29.51%. [ABSTRACT FROM AUTHOR]

Over the last decade, a plethora of training datasets have been compiled for use in language-based machine perception and in human-centered AI, alongside research regarding their compilation methods. From a primarily linguistic perspective, we add to these studies in two ways. First, we provide an overview of sixty-six training datasets used in automatic image, video, and audio captioning, examining their compilation methods with a metadata analysis. Second, we delve into the annotation process of crowdsourced datasets with an interest in understanding the linguistic factors that affect the form and content of the captions, such as contextualization and perspectivation. With a qualitative content analysis, we examine annotator instructions with a selection of eleven datasets. Drawing from various theoretical frameworks that help assess the effectiveness of the instructions, we discuss the visual and textual presentation of the instructions, as well as the perspective-guidance that is an essential part of the language instructions. While our analysis indicates that some standards in the formulation of instructions seem to have formed in the field, we also identified various reoccurring issues potentially hindering readability and comprehensibility of the instructions, and therefore, caption quality. To enhance readability, we emphasize the importance of text structure, organization of the information, consistent use of typographical cues, and clarity of language use. Last, engaging with previous research, we assess the compilation of both web-sourced and crowdsourced captioning datasets from various perspectives, discussing factors affecting the diversity of the datasets. [ABSTRACT FROM AUTHOR]

An issue in drug discovery research at pharmaceutical companies is the decline in the probability of market launch, and there is a need to improve the proof-of-concept (POC) acquisition rate by further improving clinical predictability. For this purpose, we need to deepen our understanding of human pathophysiology, and to make efficient of drug discovery research by utilizing human samples and data due to the change in mindset from "animals" to "humans" at the drug discovery research stage. In particular, with the aim of improving the efficiency of drug discovery research by utilizing human samples/data, we have established a human sample utilization support capability, then we are supporting the acquisition of appropriate human samples/data to meet each drug research need with collecting information on organizations that can provide various human samples and accumulating know-how on obtaining human samples/data. In addition, we have built a one-stop support system for surveying human samples/data, negotiating, and contracting with the organizations, obtaining ethical committee approvals, importing samples, and dealing with customs clearance. As results, our researchers can obtain high-quality human samples/data to meet their research needs rapidly/easily. By establishing this capability, the number of the research projects that implement target validation, pharmacological evaluation, BM identification, and patient stratification, etc. using human samples/data from the early stage of drug discovery, has increased, and then, the evidence obtained by using human samples/data contribute to create drug candidates with high probability of clinical effect. We will introduce our activities while showing the support flow that was actually constructed. [ABSTRACT FROM AUTHOR]

The cellular space robot (CSR) is a new type of self-reconfigurable robot. It can adapt the variety of on-orbit service tasks with large space spans through multi-level reconfiguration mechanisms. As the CSR has a large configuration space, kinematic solving becomes a key problem affecting on-orbit operation capability, and kinematic automatic solving research must be conducted. In order to solve this problem, firstly, the cellular space robot system capable of realizing multi-level self-reconfiguration is proposed for the demand of space on-orbit service, and the kinematic equations of modules are constructed by considering a single module function using screw theory. Secondly, the kinematics of the cellular space robot are encapsulated and divided into multiple levels, and the multilevel-assembly relationship-description method for robotic systems is proposed based on graph theory. On this basis, the pathway-solving algorithm is proposed to express the robot organization reachability information. Finally, the module–organ–robot multilevel kinematics solving algorithm is proposed in combination with screw theory. In order to verify the effectiveness of the algorithm in this paper, numerical simulation is used to compare with the proposed algorithm. The results show that compared with the traditional algorithm, the method in this paper only needs to update part of the assembly relations after organ migration, which simplifies the kinematic modeling operation and improves the efficiency of kinematic computation. [ABSTRACT FROM AUTHOR]

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study investigates whether the success of salary history bans could be limited by job-seekers volunteering their salaries unprompted. We survey American workers in 2019 and 2021 about their recent job searches, distinguishing when candidates were asked about salary history from when they were not. Historically well-paid workers may have an incentive to disclose, and employers who are aware of this could infer that nondisclosing workers are concealing low salaries. Through this mechanism, all workers could face pressure to avoid the stigma of silence. Our data shows a large percentage of workers (28%) volunteer salary history, even when a ban prevents employers from asking. An additional 47% will disclose if enough other job candidates disclose. Men are more likely than women to disclose their salaries unprompted, especially if they believe other candidates are disclosing. Over our 1.5-year sample covering jurisdictions with (and without) bans, unprompted volunteering of salary histories increased by about 6–8 percentage points. Funding: This work was supported by W.E. Upjohn Institute for Employment Research; Ewing Marion Kauffman Foundation (Emerging Scholars Program). Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2023.17384. [ABSTRACT FROM AUTHOR]

In a large-scale distributed network, a naming service is used to achieve location transparency and provide effective content discovery. However, fast and accurate name retrieval in the massive name set is laborious. Approximate set membership data structures, such as Bloom filter and Cuckoo filter, are very popular in distributed information systems. They obtain high query performance and reduce memory requirements through the abstract representation of information, but at the cost of introducing query error rates, which will ultimately affect content service quality. In this paper, in order to obtain higher space utilization and a lower query false positive rate, we propose a flexible fingerprint cuckoo filter (FFCF) for information storage and retrieval, which can change the length and type of fingerprints adaptively. In our scheme, FFCF uses longer fingerprints under low occupancy and has the ability to correct errors by changing the type of stored fingerprints. Moreover, we give a theoretical proof and evaluate the performance of FFCF by experimental simulations with synthetic data sets and real network packets. The results demonstrate that FFCF can improve memory utilization, significantly reduce false positive errors by nearly 90 % at 50 % occupancy and outperform Cuckoo filter in the full range of occupancy. [ABSTRACT FROM AUTHOR]

Field trips enrich learning programs with out-of-school activities that can bring gains in students' academic content knowledge and personal growth. However, they are a source of anxiety for teachers because of the bureaucracy, pedagogy, etc., risks they imply. To address this issue, we propose FieldTripOrganizer, a field trip planner based on the mixed-initiative approach aimed at increasing teachers' autonomy and motivation in designing educational tours. The key aspects of our application are (i) the simultaneous provision of information filtering and automated scheduling support while the user designs the field trip, and (ii) the visual annotation of places and activities to show whether they can be included in the itinerary without violating its time constraints. Different from current tour planners, these functions enable the user to be in full control of the design process, delegating the system to manage difficult and burdensome tasks such as consistency checks and itinerary optimization. We evaluated FieldTripOrganizer in the use case of organizing a science field trip. In a preliminary user study involving 18 science teachers, our application turned up to be superior to a baseline tour planner in both usability and user experience. Moreover, the teachers declared that it was helpful, motivated them, and reduced their anxiety during the design of the field trips. [ABSTRACT FROM AUTHOR]

This paper addresses the increasing complexity of challenges in the field of continuous nonlinear optimization by proposing an innovative algorithm called information acquisition optimizer (IAO), which is inspired by human information acquisition behaviors and consists of three crucial strategies: information collection, information filtering and evaluation, and information analysis and organization to accommodate diverse optimization requirements. Firstly, comparative assessments of performance are conducted between the IAO and 15 widely recognized algorithms using the standard test function suites from CEC2014, CEC2017, CEC2020, and CEC2022. The results demonstrate that IAO is robustly competitive regarding convergence rate, solution accuracy, and stability. Additionally, the outcomes of the Wilcoxon signed rank test and Friedman mean ranking strongly validate the effectiveness and reliability of IAO. Moreover, the time comparison analysis experiments indicate its high efficiency. Finally, comparative tests on five real-world optimization difficulties affirm the remarkable applicability of IAO in handling complex issues with unknown search spaces. The code for the IAO algorithm is available at https://ww2.mathworks.cn/matlabcentral/fileexchange/169331-information-acquisition-optimizer. [ABSTRACT FROM AUTHOR]

Advanced Persistent Threat (APT) attack is one of the most dangerous cyber-attack techniques nowadays. Therefore, the issue of detecting and predicting the spread of APT malware in the network is a very urgent issue to help the process of preventing this attack effectively. In this paper, we propose a new approach that is capable of predicting the spread of APT malware in the network based on the APT's own behaviors. Accordingly, to predict the spread of APT malicious code in the system, we propose to use a combination of two single Susceptible‐Infected‐Recovered (SIR) models. Specifically, the first SIR model was built to predict the spread of APT malicious code to devices and computers within the organization. These devices and computers are often used by APT malicious code as a basis to escalate privileges to devices or computers containing important and sensitive information of the organization. The second SIR model has the function of predicting the spread of APT malware to a group of computers containing sensitive information or potentially causing high risks to the organization. The two SIR models will provide information about infections between computer groups in the system to help accurately predict the spread of APT malware in the system. The proposal to combine two SIR models in the article is a new proposal based on the behavior of APT malware in practice. By combining two SIR models, the proposal in this article has opened up a new approach for a number of problems predicting the spread in the internet such as malicious code in wireless sensor networks or malicious information on the social network. [ABSTRACT FROM AUTHOR]

Background: In Canada, primary care reforms led to the implementation of various team-based care models to improve access and provide more comprehensive care for patients. Despite these advances, ongoing challenges remain. The aim of this scoping review is to explore current understanding of the functioning of these care models as well as the contexts in which they have emerged and their impact on the population, providers and healthcare costs. Methods: The Medline and CINAHL databases were consulted. To be included, team-based care models had to be co-located, involve a family physician, specify the other professionals included, and provide information about their organization, their relevance and their impact within a primary care context. Models based on inter-professional intervention programs were excluded. The organization and coordination of services, the emerging contexts and the impact on the population, providers and healthcare costs were analysed. Results: A total of 5952 studies were screened after removing duplicates; 15 articles were selected for final analysis. There was considerable variation in the information available as well as the terms used to describe the models. They are operationalized in various ways, generally consistent with the Patient's Medical Home vision. Except for nurses, the inclusion of other types of professionals is variable and tends to be associated with the specific nature of the services offered. The models primarily focus on individuals with mental health conditions and chronic diseases. They appear to generally satisfy the expectations of the overarching framework of a high-performing team-based primary care model at patient and provider levels. However, economic factors are seldom integrated in their evaluations. Conclusions: The studies rarely provide an overarching view that permits an understanding of the specific contexts, service organization, their impacts, and the broader context of implementation, making it difficult to establish universal guidelines for the operationalization of effective models. Negotiating the inherent complexity associated with implementing models requires a collaborative approach between various stakeholders, including patients, to tailor the models to the specific needs and characteristics of populations in given areas, and reflection about the professionals to be included in delivering these services. [ABSTRACT FROM AUTHOR]

In a trajectory management system, moving objects are typically equipped with GPS devices to report their locations to a data store. Given the potentially high frequency of location updates by multiple moving objects, these data stores often operate under write-intensive conditions. Existing trajectory indexing methods, such as XZ-ordering, which are geared towards static trajectory data, may experience considerable latency under such demanding workloads. In response, this paper introduces spatio-temporal index structures that can be constructed with low latency. Trajectories are categorized into two types for storage: 'live' and 'static'. Live trajectories are indexed by the Dual-Key Encoding (DKE) scheme, where each data point is represented by two key-value entries, facilitating both ID-temporal and spatial queries. Static trajectories, on the other hand, offer a more compact storage solution, reducing the overhead associated with live trajectories. Upon the completion of a trip, live trajectory data is transformed into a static trajectory entry through a compaction process. To augment the efficiency of spatial index construction for static trajectories, we introduce a new encoding scheme, XS2, coupled with an adaptive segmentation policy, AdaptSeg, to optimize trajectory segmentation, thereby enhancing index building and query processing efficiency. The indexing methods are demonstrated atop of LevelDB, an LSM-based key-value storage library, resulting the proposed LevelDBST system. Performance evaluations conducted using both synthetic and real-world datasets reveal that LevelDBST is capable of constructing spatial indexes for continuously updating trajectories with reduced latency, in comparison to traditional XZ-ordering methods. This efficiency is achieved while maintaining an acceptable balance in data accessing time and storage costs. [ABSTRACT FROM AUTHOR]

Copyright of Service Industries Journal is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Daily experiences often involve the processing of multiple sequences, yet storing them challenges the limited capacity of working memory (WM). To achieve efficient memory storage, relational structures shared by sequences would be leveraged to reorganize and compress information. Here, participants memorized a sequence of items with different colors and spatial locations and later reproduced the full color and location sequences one after another. Crucially, we manipulated the consistency between location and color sequence trajectories. First, sequences with consistent trajectories demonstrate improved memory performance and a trajectory correlation between reproduced color and location sequences. Second, sequences with consistent trajectories show neural reactivation of common trajectories, and display spontaneous replay of color sequences when recalling locations. Finally, neural reactivation correlates with WM behavior. Our findings suggest that a shared common structure is leveraged for the storage of multiple sequences through compressed encoding and neural replay, together facilitating efficient information organization in WM. [ABSTRACT FROM AUTHOR]

Introduction: The aim of the study was to improve student skills in writing goodquality synthesis texts through a strategic, self-regulated instruction program aimed at ensuring that students properly activated reading and writing strategies required by the synthesis task. Methods: The sample consisted of 84 university students who were randomly assigned to experimental or control conditions. The experimental group received an instructional program based on the development and self-regulated implementation of reading and writing strategies for producing synthesis texts. The control group received a program involving metacognitive knowledge of various academic text types. Both programs involved eight 60-min sessions, taught by teachers in a compulsory degree subject. For the evaluations, students produced synthesis texts from different source texts. The syntheses were graded considering text product measures: information selection, idea connection, text organization, and holistic quality; and measures of reading (underlining and note-taking) and writing (planning and review) strategies. Results: The results show that the experimental group exhibited greater improvements in synthesis quality and greater improvements in activation of information organization processes, note-taking while reading, and text planning. Discussion: In conclusion, university students can, following implementation of a strategic instructional procedure in the context of a study plan, adapt and rework their own reading and writing strategies and apply them in a self-regulated manner to synthesis tasks, improving text quality and some of the cognitive processes involved. [ABSTRACT FROM AUTHOR]

In today's world where information is a valuable and vital resource, Information Management emerges as a fundamental discipline to deal with the growing volume of data and transform it into strategic knowledge. This article seeks to present the development of Information Management from the emergence of Information Science and its subareas and its contribution to the management of organizations. Thus, the work unfolds into four main sections: (1) discussion of the framework of the emergence of Information Science and its main milestones over the years; (2) different concepts and characteristics of IC and the emergence of different branched subareas of IC; (3) presentation of the definitions and characteristics of the Information Management subarea; (4) exposition of the various advantages resulting from the application of Information Management in organizations. From the research it was possible to identify that the main advantages arising from information management contribute especially to a better understanding of the different organizational areas and help in decision making. [ABSTRACT FROM AUTHOR]

Purpose: This paper aimed to analyze the technical efficiency of the Federal Network of Professional, Scientific and Technological Education for the year 2020, by comparing the data with the previous three years. By 2020, the institutions suspended face-to-face academic activities as a preventive measure to avoid contagion from Sars-CoV-2, a virus that causes the Covid-19. Methodology: The research presents a quantitative approach and examined 41 institutions between 2017 and 2020 using the Data Envoltory Analysis (DEA) method. Results: The technical efficiency of 2018 was the lowest among the analyzed years, but the 2020 numbers are less satisfactory than the years between 2017 and 2019. In 2020, 29,3% of the institutions were considered inefficient or with poor efficiency. However, on average, the units maintained the pattern of technical efficiency recorded since 2017. Contributions of the Study: The results contribute to the transparency of public organizations; information on the cost-benefit ratio of public services; identify best practices in the use of public resources, that can help decision-making in inefficient units; and show that there is a need for improvements in educational management. [ABSTRACT FROM AUTHOR]

In today’s knowledge-based and rapidly changing society, developing students’ critical thinking skills in instruction is a vital concern in almost all science curricula. To acquire meaningful learning, learners need to develop skills in choosing suitable information, gauging the integrity of information, and crafting sound decisions. With this, creating learning spaces that stimulate critical thinking has become a challenge among educators. This study aims to explore the critical thinking profile of Filipino students in science laboratory classes as a means of generating insight for possible curricular enhancement. Utilizing a quantitative descriptive research design, a survey was administered to 300 junior high school students under K to 12 curricula. Results revealed that openness to different ideas is the most practiced aspect of critical thinking among Filipino students, while the planning and organization of information disclosed as the least. Furthermore, the importance of gathering information to support position is the most agreed factor, while the strategies in thinking of a problem in an experiment are the least. Hence, findings suggest that the necessary facets of critical thinking strategies should be explicitly articulated in the science curriculum framework, especially encapsulating it in the content and performance standards. [ABSTRACT FROM AUTHOR]

Copyright of Computer und Recht is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of CIRCULO de Linguistica Aplicada a la Comunicacion is the property of Universidad Complutense de Madrid and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We may incorrectly assume that because many of our students were "born digital," and have never known a time without computers in their schools or classrooms, they are innately digitally literate. However, interactions with students at all levels in a variety of settings such as introductory undergraduate courses, graduate seminars, and technology-focused workshops have demonstrated that some students struggle with or are unaware of basic computing and digital literacy concepts like file management and information organization. This article details three unique case studies and explores how librarians liaising with different subject areas have worked to incorporate digital literacy objectives into their instruction and outreach initiatives. Inclusive learning opportunities for librarians and instructors to enhance students' digital literacy skills are recommended. [ABSTRACT FROM AUTHOR]

The digitalisation of indigenous knowledge has been challenging considering epistemological differences and the lack of involvement of indigenous people. Drawing from our most recent community projects in Namibia, we share insights on indigenous ecospatial worldviews guiding the design of digital information organization and access of indigenous knowledge. With emerging technologies, such as augmented and virtual reality, offering new opportunities for richer and more meaningful spatial and embodied accounts of indigenous knowledge, we re-imagine digital libraries inclusive of indigenous people and their worldviews. [ABSTRACT FROM AUTHOR]

Throughout history, human beings have developed a variety of strategies to traverse the enormous knowledge space and locate specific information. Taking metaphorical use of space as a means of conceptualizing information, namely where numerous information reside could be regarded as a vast space, and the trail navigating through the space to find information could be seen as an information path. The referenceable order or structure within information space is crucial in the process of information revisiting. From oral culture, written culture to bits culture, the research intends as a review of diverse methods of information organization, storage and retrieval developed in Western civilization. By looking at representative knowledge management systems in the three cultures, the discussion would be approached from the perspectives of mnemotechnics, library science and computer science. [ABSTRACT FROM AUTHOR]

This teaching case describes, based on publicly available material, the transition of the A. P. Moller Maersk shipping company from a logistics-based organization to an information and technology-based organization. Multiple digital technologies, including distributed ledger, robotic automation, digital platforms, and big-data analytics offered opportunities for Maersk. Although the company was a relatively late starter in digital transformation, its journey so far has been successful. The challenge is how to enact sustained digital transformation in an environment of rapid technology change. Some theoretical lenses are offered to assist with analyzing and framing Maersk's digital strategy, including business models, organizational ambidexterity, and dynamic capabilities. [ABSTRACT FROM AUTHOR]

Copyright of Service Industries Journal is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The development of autonomous vehicles (AV) entails complex design, production, testing, and deployment procedures that require excellent information and knowledge management in the event of security breaches. As researchers and managers, we must have a good understanding of the development of AV technology. Communication and information exchange, as well as knowledge management (KM) strategies and approaches, are undoubtedly important components. The most difficult challenge is to save the lives of drivers and passengers who use and travel in an AV that is partially or entirely managed by a machine learning model (ML) and artificial intelligence (AI). In this paper, we attempt to investigate the manufacturing process of AVs and intelligent vehicles (IVs), emphasizing the importance of information management (IM) within the factory and processes that are not explicitly articulated in the majority of scenarios. Furthermore, we discuss our method for using the knowledge management life cycle for information exchange in an organization, which could allow for faster and more efficient resolution of security issues within vehicle operating systems. This study seeks to provide an essential theoretical foundation to characterize the future scope of integrated manufacturing, which integrates software and the industry that employs it within the vehicle. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Librarianship & Information Science is the property of Sage Publications, Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Inappropriate lifestyles are present in work environments that are significant challenges to the health and wellbeing of workers, especially in office environments. The review aims to comprehensively identify and analyze the effect of WPPAs (physical activity-based workplace wellness programs) on employee productivity and health and to provide information to organizations on the importance, benefits, and possible implementation strategies. A total of 321 original open access articles were identified from the SCOPUS database and 25 publications met the inclusion criteria. The (PICOC) strategy was used to define the eligibility criteria. The findings showed positive effects of physical activity on cardiovascular health, muscle strength, and work productivity. Each type of physical exercise could not be analyzed in depth because of their differences in terms of methodologies or population and since the research did not seek to evaluate the characteristics of these exercises, economic comparison of the investment was not performed as few sources offered such information. Although WPPAs hold promise for improving health and productivity at work, greater standardization is required in identifying the characteristics of physical activity, monitoring and understanding its long-term impacts to maximize its effectiveness and sustainability. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to explore how source type can influence organizational assets proposed by source credibility theory (SCT) when paired with matched situational crisis communication theory (SCCT) strategies for accidental, preventable, and victim crises. Crisis communication delivered online provides an invaluable outlet for organizations to disperse information to stakeholders quickly. It has been shown that receivers of this information have motivational assumptions about sources having their own agenda for producing content. Thus, it is important to explore how sources tasked with delivering crisis responses can influence perceptions of the sincerity and credibility of the message. Design/methodology/approach: The researchers conducted a 3 (crisis response: matched accidental, matched preventable, matched victim) × 3 (source type: organization, CEO, The New York Times) online between-subjects experimental design (N = 623). Findings: By identifying how the source disseminating crisis responses influences message perceptions, findings from this study recognize how the crisis response is situated in a greater context. Since perceived sincerity and credibility were found to influence message acceptance and reputation, making intentional decisions that acknowledge both within a crisis communication strategy may benefit both future practice and research applications. Originality/value: The current study advances understandings afforded by SCCT, along with SCT, by experimentally testing the influence of these variables within crisis responses on outcomes such as account acceptance and organizational reputation. [ABSTRACT FROM AUTHOR]

The article "Muscle déjà vu: are myonuclei the blueprint for muscle regrowth?" published in the Journal of Physiology explores the concept of muscle memory in skeletal muscle. The study suggests that muscle fibers retain an increased number of myonuclei, which support muscle growth and repair, even after periods of disuse-induced atrophy. The research provides evidence for myonuclear accretion and permanence, as well as changes in gene expression, shedding light on the molecular mechanisms underlying muscle memory. Further research is needed to explore the relationship between muscle memory and the recovery of muscle function and performance, as well as the potential effects of performance-enhancing substances on these mechanisms. [Extracted from the article]

This study presents the results of an online survey that benchmarked the organization of information technology (IT) functions in academic library members of the Association of Research Libraries. The survey investigated whether responsibility for 14 key areas resided in the libraries or in an institution-level information technology department, whether responsibilities have shifted over the past 20 years, satisfaction with services provided, assessment methods used to evaluate information technology services, and top challenges facing library IT. This paper considers the survey results addressing where the responsibility lies, satisfaction with the support, and whether and when the responsibility changed. [ABSTRACT FROM AUTHOR]

Purpose: The aim of the present study is to reconcile the dual embodiment of ontology and folksonomy and establish a symbiotic relationship between the two technologies for the development of folksonomized ontology as a tool for organizing information in the fields of psychology and educational sciences in cyberspace.Method: This study is of an applied nature and utilizes a combined research method. To gain conceptual knowledge and develop a semi-automated ontology based on natural language (common labels), the three-step method proposed by Alves and Santanche (extraction, enrichment, and synthesis) (2013) is utilized, along with Noy and McGuinness's approach (2001). The statistical population refers to the labels used by students of psychology and educational sciences in the digital library of Allameh Tabatabai University. The purposive sampling method is used to select and aggregate specific labels. Additionally, to evaluate the credibility and validity of the extracted concepts and relationships, they are reviewed and confirmed by 15 experts in the fields of psychology and educational sciences. The software used to create the ontology is Protégé version 5.5.Findings: A total of 15,787 labels were extracted. After identifying the fundamental concepts and their interrelationships using the Eric database, a conceptual framework with an ontological approach was developed. After confirming and validating the conceptual structure, an ontology based on folksonomy is presented as the main research finding. It consists of 277 classes and subclasses, 110 relation types, 50 samples, and 9 synonyms taken from the Eric database, and is illustrated in several graphs. Based on the statistical test performed on the ontology derived from folksonomy with a significance level of 0.0005, the model has been proven to be relatively accurate, and all components have been confirmed by experts.Conclusion: Folksonomies are "user-generated ontologies" within the Semantic Web. Leveraging the structural distinctions between folksonomies and ontologies holds the potential to advance the Semantic Web without requiring a deep understanding of complex classification systems.

The world is changing increasingly faster, bringing uncertainty and complexity to many processes, and this acceleration leads to the rise of demand for studies regarding the future. Futures Studies have been widely used to support decision-making, emphasizing forecasting technologies' timing, diffusion, and life cycle. Therefore, evaluating previous forecasts to understand their proposed scenarios better is often necessary. This work analyzes all articles published until the end of 2023 in one of the most relevant journals of this field of study, the European Journal of Futures Research (EJFR). The EJFR is among the main contributors to fostering dialogue among scholars, practitioners, and policymakers interested in anticipating and shaping the future. We streamlined the uncovering of future events written on EJFR articles using NERMAP, a software we designed to semi-automate the roadmapping process. We built five simple scenarios and associated each of the 214 future events found into a scenario. Then, we analyzed the 52 events that were supposed to happen until 2023 to discover how the expected possible events did occur in real life. Therefore, we expect to provide insights, better understand how expected possible events occur in real life, and write a novel way to understand the journal's history. The methodology used in this study is novel and applicable to other studies to reduce the time and risk of errors when developing large-scale roadmaps. This work provides valuable information for decision-makers in organizations and governments. The Futures Research scientific community can use the results to improve their research frameworks. [ABSTRACT FROM AUTHOR]

Objective: Few studies have investigated the maintenance of skills acquired in classroom-based clinician education. Using an advanced simulation-based clinical communication skill training program for postgraduate psychiatry education (ComPsych), we aimed to investigate skill acquisition through assessing changes in competence (abilities) and performance (practice). Methods: Forty trainee psychiatrists (15 males; age range 26–48) participated. Video-recorded Standardized Patient Assessments (SPAs) were delivered twice pre- and post-training to assess learning. Skills were coded by independent psychologists using the Core Communication Skills (CCS) coding system. Simulated patients (SPs) rated trainees' communication performance using the Sim-Patient SPA checklist. Paired t-tests, linear mixed models and logistic mixed models assessed changes in communication skills over time. Results: For SPAs, reliability of coder ratings was deemed acceptable (ICC range 0.67 to 0.87). Mean post-training communication performance significantly increased for skills in agenda setting (p < 0.001), information organization (p < 0.001), empathic skills (p = 0.046), and overall skills performance (p = 0.001). Significant decreases for questioning skills were indicative of reduced reliance on these skills post-training. SPs rated all skillsets higher post-training. A modest relationship was detected between frequency (coded) and (SP-rated) quality of communication skills. Improvements in agenda setting and information organisation skills were retained ~ 6 weeks post-training. Conclusions: Training improved patient-centered communication skills in psychiatry trainees, particularly skills in agenda setting and information organization, with skills retained ~ 6 weeks post-training. There was reduced reliance on questioning skills, which are well utilised generally. The study supports the benefits of this method of communication skills training into postgraduate psychiatry education. [ABSTRACT FROM AUTHOR]

Purpose: The aim of the present study is to reconcile the dual embodiment of ontology and folksonomy and establish a symbiotic relationship between the two technologies for the development of folksonomized ontology as a tool for organizing information in the fields of psychology and educational sciences in cyberspace. Method: This study is of an applied nature and utilizes a combined research method. To gain conceptual knowledge and develop a semi-automated ontology based on natural language (common labels), the three-step method proposed by Alves and Santanche (extraction, enrichment, and synthesis) (2013) is utilized, along with Noy and McGuinness's approach (2001). The statistical population refers to the labels used by students of psychology and educational sciences in the digital library of Allameh Tabatabai University. The purposive sampling method is used to select and aggregate specific labels. Additionally, to evaluate the credibility and validity of the extracted concepts and relationships, they are reviewed and confirmed by 15 experts in the fields of psychology and educational sciences. The software used to create the ontology is Protégé version 5.5. Findings: A total of 15,787 labels were extracted. After identifying the fundamental concepts and their interrelationships using the Eric database, a conceptual framework with an ontological approach was developed. After confirming and validating the conceptual structure, an ontology based on folksonomy is presented as the main research finding. It consists of 277 classes and subclasses, 110 relation types, 50 samples, and 9 synonyms taken from the Eric database, and is illustrated in several graphs. Based on the statistical test performed on the ontology derived from folksonomy with a significance level of 0.0005, the model has been proven to be relatively accurate, and all components have been confirmed by experts. Conclusion: Folksonomies are "user-generated ontologies" within the Semantic Web. Leveraging the structural distinctions between folksonomies and ontologies holds the potential to advance the Semantic Web without requiring a deep understanding of complex classification systems. [ABSTRACT FROM AUTHOR]

Insider threats remain a serious anxiety for organizations, government agencies, and businesses. Normally, the most hazardous cyber attacks are formed by trusted insiders and not by malicious outsiders. The malicious behaviors resulting from unplanned or planned mishandling of resources, data, networks, and systems of an organization constitute an insider threat. The unsupervised behavioral anomaly detection methods are mostly developed by the traditional machine learning methods for identifying unusual or anomalous variations in user behavior. The insider threat mainly originates from an individual inside the organization who is a current or former employee who has access to sensitive information about the organization. For achieving an improvement over traditional methods, the Stacked Convolutional Neural Network- Attentional Bi-directional Gated Recurrent Unit model is proposed in this paper to detect insider threats. The CNN-Attentional BiGRU model utilizes the user activity logs and user information for time-series classification. Using the log files, the temporal data representations, and weekly and daily numerical features from various sub-models of CNN are learned by the stacked generalization. Based on the chosen feature vectors, a model is trained on the CERT insider threat dataset. The stacked CNN is combined with the Attentional BiGRU model to incorporate more complex features of the user activity logs and user data during each convolution operation without raising network parameters. Thus the classification performance is improved with less complexity. The non-linear time control, chaos-based strategy, update rules, and opposite-based learning strategies are evaluated for generating the Modified-Equilibrium Optimization. The simulation outputs obtained by the model are 92.52% accuracy, 98% Precision, 95% Recall, and 96% F1-score. Thus, the proposed model has reached higher detection performance. [ABSTRACT FROM AUTHOR]

This article discusses the potential for using the Viable System Model (VSM) as a new approach in developing accounting information systems. This article takes the form of a systematic literature review. The analysis was carried out using a comparative approach between the System Development Life Cycle (SDLC-Model) and the Viable System Model (VSM) in general and specifically in the field of accounting and accounting information systems. The SDLC is primarily focused on the development and management of software or systems, while the VSM is concerned with the broader understanding and management of complex organizations. Both concepts have their unique applications and are important in their respective domains. The results of the study show that the VSM has a more holistic approach in developing accounting information systems. The research results also show that VSM is more suitable for developing accounting information systems in organizations that are complex, flexible, and adaptive to the speed of change in the company's ongoing organizational business environment. The VSM provides a framework for understanding how organizations and systems can adapt to changing environments and maintain viability. It is particularly useful for complex organizations that need to respond to dynamic conditions. [ABSTRACT FROM AUTHOR]

Knowledge management is the process of managing information within organizations with the aim of improving performance and increasing productivity. With the emergence of generative artificial intelligence, it has become possible to employ it to improve knowledge management and transform it into tangible value for organizations, as generative artificial intelligence can be used in knowledge management to improve the performance of organizations and increase the value of knowledge in them in various fields. In this review of the literature on the subject, the focus will be on the fields of knowledge management and generative artificial intelligence to determine the beginning of the emergence of the subject and the associated concepts, the research interests or theories that dealt with the subject, the beginning of studies on the subject and how they developed, the conferences, workshops and scientific societies that dealt with the subject, and the digital index to track terms. Topic in databases in digital search engines. [ABSTRACT FROM AUTHOR]

Libraries have long been essential for democratizing knowledge and providing reliable information, extending their services to meet diverse community needs, including educational programs and internet access (Pawley, 2022; Freudenberger, 2022). Librarians, as custodians of information and culture, possess core competencies in information organization, digital literacy, and research skills. The San José State University (SJSU) School of Information (iSchool) aims to educate professionals who significantly impact global communities through high-quality education, research, and technology innovation. This article examines the Master of Library and Information Science (MLIS) program at SJSU iSchool, focusing on the evolution of its 14 core competencies to incorporate advancements in artificial intelligence (AI). As AI transforms educational curricula and administrative processes, updates are needed to include digital literacy, AI ethics, and data privacy, ensuring MLIS graduates are equipped to lead in an AI-integrated future. By aligning these competencies with the iSchool's strategic vision, the article provides suggestions for integrating AI into existing competencies and identifies potential new competencies to address emerging workplace needs effectively. [ABSTRACT FROM AUTHOR]